Brian DeCost

4.6k citations

46 papers · 2.9k indexed · 2 hit papers · h-index 20

Impact in

Metals and Alloys top 5%
- Hydrogen embrittlement and corrosion behaviors in metals
Structural Biology top 5%

Papers in ⓘ

Materials Chemistry 36
- Machine Learning in Materials Science 32
- X-ray Diffraction in Crystallography 11
- Electronic and Structural Properties of Oxides 6
- Corrosion Behavior and Inhibition 3
Mechanical Engineering 12
- High Entropy Alloys Studies 5
- Mineral Processing and Grinding 3

Co-authors: Kamal Choudhary (15 shared papers)Elizabeth A. Holm (9 shared papers)Francesca Tavazza (5 shared papers)Toby Francis (2 shared papers)Ankit Agrawal (2 shared papers)Alok Choudhary (2 shared papers)Simon J. L. Billinge (2 shared papers)Chris Wolverton (2 shared papers)
Journals: npj Computational Materials (6 papers)Matter (3 papers)Computational Materials Science (2 papers)JOM (2 papers)Molecular Systems Design & Engineering (2 papers)
Partner nations: United States Canada Singapore

In The Last Decade

Brian DeCost

45 papers receiving 2.8k citations

Hit Papers

align trajectories log scale

Recent advances and applications of deep learning methods in materials science 2022 · 652 citations

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if any of the following hold:

it has ≥500 total citations;
it reaches ≥1.5× the top-1% citation threshold for papers in the same subfield and year (the threshold is the minimum needed to enter the top 1%, not the average within it);
it reaches the top citation threshold in at least one of its specific research topics.

2022 npj Computational Materials
2021 npj Computational Materials

Peers

Countries citing papers authored by Brian DeCost

Since Specialization

Citations

This map shows the geographic impact of Brian DeCost's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Brian DeCost with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Brian DeCost more than expected).

Fields of papers citing papers by Brian DeCost

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Brian DeCost. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Brian DeCost. The network helps show where Brian DeCost may publish in the future.

Co-authors

The 25 scholars most cited alongside Brian DeCost, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Brian DeCost Line = papers co-authored together Brian DeCost links everyone, so they are left out of the graph.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Showing the 20 most-cited of 46 papers — load more, or switch the sort, to bring in the rest.

#	Work
1	Recent advances and applications of deep learning methods in materials science npj Computational Materials ·Kamal Choudhary, Brian DeCost, Chi Chen, Anubhav Jain, Francesca Tavazza, Ryan Cohn, Cheol Woo Park, Alok Choudhary, Ankit Agrawal, Simon J. L. Billinge, Elizabeth A. Holm, Shyue Ping Ong, Chris Wolverton Hit paper breakdown →	2022	652
2	Atomistic Line Graph Neural Network for improved materials property predictions npj Computational Materials ·Kamal Choudhary, Brian DeCost Hit paper breakdown →	2021	415
3	Accelerated Development of Perovskite-Inspired Materials via High-Throughput Synthesis and Machine-Learning Diagnosis Joule ·Shijing Sun, Noor Titan Putri Hartono, Zekun Ren, Felipe Oviedo, Antonio M. Buscemi, Mariya Layurova, De Xin Chen, Tofunmi Ogunfunmi, Janak Thapa, Savitha Ramasamy, Charles Settens, Brian DeCost, A. Gilad Kusne, Zhe Liu, Siyu Tian, Ian Marius Peters, Juan‐Pablo Correa‐Baena, Tonio Buonassisi	2019	250
4	Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks npj Computational Materials ·Felipe Oviedo, Zekun Ren, Shijing Sun, Charles Settens, Zhe Liu, Noor Titan Putri Hartono, Savitha Ramasamy, Brian DeCost, Siyu Tian, Giuseppe Romano, A. Gilad Kusne, Tonio Buonassisi	2019	242
5	A computer vision approach for automated analysis and classification of microstructural image data Computational Materials Science ·Brian DeCost, Elizabeth A. Holm	2015	216
6	Exploring the microstructure manifold: Image texture representations applied to ultrahigh carbon steel microstructures Acta Materialia ·Brian DeCost, Toby Francis, Elizabeth A. Holm	2017	160
7	Machine learning with force-field-inspired descriptors for materials: Fast screening and mapping energy landscape Physical Review Materials ·Kamal Choudhary, Brian DeCost, Francesca Tavazza	2018	118
8	Computer Vision and Machine Learning for Autonomous Characterization of AM Powder Feedstocks JOM ·Brian DeCost, Harshvardhan Jain, Anthony D. Rollett, Elizabeth A. Holm	2016	101
9	Unified graph neural network force-field for the periodic table: solid state applications Digital Discovery ·Kamal Choudhary, Brian DeCost, Lily Major, Keith T. Butler, Jeyan Thiyagalingam, Francesca Tavazza	2023	72
10	A critical examination of robustness and generalizability of machine learning prediction of materials properties npj Computational Materials ·Kangming Li, Brian DeCost, Kamal Choudhary, Michael Greenwood, Jason Hattrick‐Simpers	2023	68
11	Building data-driven models with microstructural images: Generalization and interpretability Julia Ling, Maxwell Hutchinson, Erin Antono, Brian DeCost, Elizabeth A. Holm, Bryce Meredig	2017	63
12	UHCSDB: UltraHigh Carbon Steel Micrograph DataBase Integrating materials and manufacturing innovation ·Brian DeCost, Matthew D. Hecht, Toby Francis, Bryan A. Webler, Yoosuf N. Picard, Elizabeth A. Holm	2017	59
13	Exploiting redundancy in large materials datasets for efficient machine learning with less data Nature Communications ·Kangming Li, Daniel Persaud, Kamal Choudhary, Brian DeCost, Michael T. Greenwood, Jason Hattrick‐Simpers	2023	54
14	Characterizing powder materials using keypoint-based computer vision methods Computational Materials Science ·Brian DeCost, Elizabeth A. Holm	2016	47
15	Structure-aware graph neural network based deep transfer learning framework for enhanced predictive analytics on diverse materials datasets npj Computational Materials ·Vishu Gupta, Kamal Choudhary, Brian DeCost, Francesca Tavazza, Carelyn E. Campbell, Wei‐keng Liao, Alok Choudhary, Ankit Agrawal	2024	44
16	Why big data and compute are not necessarily the path to big materials science Communications Materials ·Naohiro Fujinuma, Brian DeCost, Jason Hattrick‐Simpers, S. E. Lofland	2022	31
17	Phenomenology of Abnormal Grain Growth in Systems with Nonuniform Grain Boundary Mobility Metallurgical and Materials Transactions A ·Brian DeCost, Elizabeth A. Holm	2016	28
18	Elucidating multi-physics interactions in suspensions for the design of polymeric dispersants: a hierarchical machine learning approach Molecular Systems Design & Engineering ·Aditya Krishna Menon, Chetali Gupta, Kedar M. Perkins, Brian DeCost, Nikita Budwal, Renee T. Rios, Kun Zhang, Barnabás Póczos, Newell R. Washburn	2017	25
19	Recent progress in the JARVIS infrastructure for next-generation data-driven materials design Applied Physics Reviews ·Daniel Wines, Ramya Gurunathan, Kevin F. Garrity, Brian DeCost, Adam J. Biacchi, Francesca Tavazza, Kamal Choudhary	2023	23
20	Accelerating defect predictions in semiconductors using graph neural networks SHILAP Revista de lepidopterología ·Md. Habibur Rahman, Prince Gollapalli, Panayotis Manganaris, Satyesh Kumar Yadav, Ghanshyam Pilania, Brian DeCost, Kamal Choudhary, Arun Mannodi‐Kanakkithodi	2024	21

About Brian DeCost

Brian DeCost is a scholar working on Materials Chemistry, Mechanical Engineering, Computational Theory and Mathematics, Electrical and Electronic Engineering and Computer Vision and Pattern Recognition, having authored 46 papers that have together received 2.9k indexed citations. Recurring topics across this work include Machine Learning in Materials Science (32 papers), X-ray Diffraction in Crystallography (11 papers), Computational Drug Discovery Methods (10 papers), Electronic and Structural Properties of Oxides (6 papers), High Entropy Alloys Studies (5 papers), High-Temperature Coating Behaviors (3 papers), Corrosion Behavior and Inhibition (3 papers) and Mineral Processing and Grinding (3 papers). The work is most often cited by research in Metals and Alloys (128 citations), Structural Biology (60 citations), Materials Chemistry (1.9k citations), Computational Theory and Mathematics (346 citations) and Surfaces, Coatings and Films (135 citations). Brian DeCost has collaborated with scholars based in United States, Canada and Singapore. Frequent co-authors include Kamal Choudhary, Elizabeth A. Holm, Francesca Tavazza, Toby Francis, Ankit Agrawal, Alok Choudhary, Simon J. L. Billinge, Chris Wolverton, Anubhav Jain and Shyue Ping Ong. Their work appears in journals such as npj Computational Materials, Matter, Computational Materials Science, JOM and Molecular Systems Design & Engineering.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

Explore authors with similar magnitude of impact